Degree Name

Department

NACO controlled Corporate Body

First Advisor

O. Patrick Kreidl, Ph.D.

Second Advisor

Alan Harris, Ph.D.

Third Advisor

Chris Brown, Ph.D., P.E.

Department Chair

Murat Tiryakioglu, Ph.D., CQE

College Dean

Mark A. Tumeo, Ph.D., J.D., P.E.

Abstract

Mathematical models are used in engineering and the sciences to estimate properties of systems of interest, increasing our understanding of the surrounding world and driving technological innovation. Unfortunately, as the systems of interest grow in complexity, so to do the models necessary to accurately describe them. Analytic solutions for problems with such models are provably intractable, motivating the use of approximate yet still accurate estimation techniques. Particle filtering methods have emerged as a popular tool in the presence of such models, spreading from its origins in signal processing to a diverse set of fields throughout engineering and the sciences including medical research, economics, robotics, and geophysics.

In groundwater hydrology, a key component of aquifer assessment is the determination of the properties which permit water resource managers to estimate aquifer drawdown and safe yield. Presented is a particle filtering approach to estimate aquifer properties from transient data sets, leveraging recently published analytically-derived models for confined aquifers. The approach is examined experimentally through validation against three common aquifer testing problems: determination of (i) transmissivity and storage coefficient from non-leaky confined aquifer performance tests, (ii) transmissivity, storage coefficient, and vertical hydraulic conductivity of a confining unit from leaky confined aquifer performance tests, and (iii) transmissivity and storage coefficient from non-leaky confined aquifer performance tests with noisy data and boundary effects. The first two problems are well-addressed and the presented approach compares favorably to the results obtained from other published methods. The third problem, which the presented method can tackle more naturally than previously-published methods, underscores the flexibility of particle filtering and, in turn, the promise such methods offer for a myriad of other geoscience problems